Multiple elastic feature net and method for target deghosting and tracking

ABSTRACT

A multiple elastic feature network having N independent sets of M feature specific neurons that respond to the temporal properties of a number of targets. The targets are represented by sets of M feature specific coordinates. Each of the neurons and its coordinates are labeled with a different one of the feature types, and have an associated receptive field and distortion, locking and expectation parameters. A sequence of candidate coordinates, which include the targets&#39; coordinates as well as false or ghost coordinates, is input to the MEFN. Each successive candidate coordinate selects the closest neuron that has the same feature type and whose receptive field includes the candidate coordinates. The coordinates of the selected neuron and the other neurons in its set are adjusted towards the candidate coordinates. The distortion, expectation and locking parameters measure the distortion in the neurons&#39; coordinates, the elapsed time since each neuron was last selected and the changes in the neurons&#39; coordinates, respectively. As the sets of neurons converge towards respective targets, the expectation, distortion and locking parameters are reduced, and hence the receptive fields are reduced, causing the neurons to lock onto and track the targets.

This is a division of application Ser. No. 08/311,373 filed Sep. 23,1994 now U.S. Pat. No. 5,680,514.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to deghosting and trackingtargets, and more specifically to a multiple elastic feature net (MEFN)and method for deghosting and tracking targets.

2. Description of the Related Art

Historically, electronic tracking systems were able to compute and tracka target's coordinates by measuring its angle of approach and range. Insome modern systems only the target's approach angle is available. Forexample, the range information for aircraft may be electronicallyjammed, rendering conventional tracking systems inoperable.

Ideally two sensors could be used to detect the target from angle-onlydata. The intersection of their respective angles would specify thetarget's coordinates. However, the detected angles are typically noisy,reducing the precision and reliability of the coordinates. Furthermore,when there is more than one target the sensors will produce false or"ghost" coordinates. For example, if at a given time the first sensordetects a target and the second sensor detects another target, theintersection of their angles will identify a ghost target, i.e. one thatdoesn't exist. The number of ghost coordinates and the complexity of thetracking problem is an exponential function of the number of targets.

A practical angle-only tracking system should be able to differentiatethe "real" targets from the ghost targets, provide accurate coordinatesand reliably lock onto the moving targets. The complexity of the systemmust be low enough to handle a large number of targets and respondaccurately in real-time.

U.S. Pat. No. 4,621,267 to Wiley, "Bearing Intersection Deghosting byAltitude Comparison System and Methods", discloses a two antenna X-beamsystem for locating N targets. Each antenna receives signals reflecteddirectly off of a target and signals reflected off of the target via anintermediate remote surface. Each antenna receives the signals on twodifferent planes such that the signal detected at one plane has a timedelay with respect to the signal detected at the other plane. By knowingthe two time delays for each antenna, the altitude of each target asseen from each of the antenna systems can be calculated. The bearingangles are determined for each target at each antenna, and the actual Ntarget locations of the N² possible locations are determined by locatingthe intersection of lines defined by the bearing angles for which theassociated altitudes for both antennas are equal.

This system is a computationally complex system. Its complexityincreases as the square of the number of targets, and hence it has alimited capacity for providing timely targeting information. Thissystem, like most others, approaches the dynamic tracking problem as asequence of static problems. Each target is detected and its coordinatesare computed without the benefit of prior tracking information.

The static approach is typically used because the methods are toocomplicated to efficiently track and update the targeting information inreal time. In these systems, the time between iterations is too great tomaintain a significant correlation between the targets' positions. Hencethe systems are not designed to utilize the dynamic characteristicsassociated with the tracking problem. These types of systems typicallyhave a low target capacity (less than 20), relatively high error rates,i.e., identify ghost targets, and are brittle. Missing, incomplete ornoisy data, which are common problems in all practical systems, cancause these systems to fail.

Neural network architectures have been used to solve a variety ofoptimization problems. Tuevo Kohonen, "Self-Organization & AssociativeMemory," Springer-Verlag, 3rd edition 1989 pp. 127-133 discloses aSelf-Organizing Feature Map (SOFM) that uses a single layer and set ofneurons. The neurons form a topographic map of the input signals, inwhich the most important similarity relationships among the inputsignals are converted into spatial relationships among the respondingneurons. The SOFM is trained for a specific application by randomizingthe coordinates of the neurons and setting their receptive fields tocover the entire input space. The receptive field defines the radiusaround the neuron to which it will respond to an input signal. Theneurons respond to each input signal that falls within their receptivefields by moving towards the input signal. The final spatialdistribution of the neurons mirrors the distribution of the inputsignals. If Kohonen's network was applied to the deghosting and targettracking problem, the neuron's would reflect the spatial distribution ofthe candidate coordinates. They would not converge and track the realtargets.

SUMMARY OF THE INVENTION

The present invention seeks to provide a robust, low complexity, highcapacity neural network and method for real-time deghosting and trackingof targets.

This is accomplished with a multiple elastic feature network (MEFN)having N independent sets of M feature specific neurons that respond tothe temporal properties of the targets. Each of the neurons and itscoordinates are labeled with a different one of the feature types, andhave an associated receptive field and distortion, locking andexpectation parameters. Each of the targets is represented by a set of Mcoordinates, which are labeled with a different one of the featuretypes. A sequence of candidate coordinates, which includes the targets'coordinates as well as false or ghost coordinates, is input to the MEFN.

Each successive candidate coordinate selects the closest neuron that hasthe same label and whose receptive field includes the candidatecoordinates. The coordinates of the selected neuron and the otherneurons in its set are adjusted towards the candidate coordinates. Thedistortion, expectation and locking parameters measure the distortion inthe neurons' coordinates, the elapsed time since each neuron was lastselected and the changes in the neurons' coordinates, respectively. Eachneuron's receptive field size is directly proportional to theexpectation, distortion and locking parameters. As the sets of neuronsconverge towards respective targets, the expectation, distortion andlocking parameters are reduced, and hence the receptive fields arereduced, causing the neurons to lock onto and track the targets.

For a better understanding of the invention, and to show how the samemay be carried into effect, reference will now be made, by way ofexample, to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a target deghosting and tracking system inaccordance with the present invention;

FIGS. 2a and 2b are diagrams of the point and area type objects;

FIG. 3 is a block diagram that illustrates the generation of inputcoordinates using angle-only data;

FIG. 4 is a diagram of a targeting scene for the three sensor multipletarget system shown in FIG. 3;

FIG. 5 is a diagram of the MEFN used in the present invention to deghostand track the targets;

FIG. 6 is a flowchart for the MEFN shown in FIG. 5;

FIG. 7 is a flowchart for the post-processor and display shown in FIG.1;

FIG. 8 is a diagram illustrating one iteration of the MEFN; and

FIGS. 9a through 9c are diagrams that illustrate the MEFN's initialstate, a partially converged state and the converged state.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a block diagram of a target deghosting and tracking system 10.A scene 12 such as a radar, visible or acoustical image, contains aplurality of stationary or moving targets 14 such as airplanes. The timevarying scenes can be analyzed as a sequence of independent still framesor as a single time varying signal. The temporal properties of the timevarying signal can be used to track the targets. The targets can beclassified either as point or area objects. As shown in FIG. 2a thepoint objects 15 are characterized by the coordinates 16 of theircentroids. As shown in FIG. 2b the area objects 17 have a specific sizeand shape and are described by sets of labeled coordinates 16, whichcorrespond to specific points on a scaled template of the object. Ingeneral the coordinate space can be two, three or four-dimensional, inwhich a time index provides the fourth dimension.

In FIG. 1 a sensor 18 extracts features 20 from the scene 12, assignslabels 22 to the features and computes their associated candidatecoordinates 24. Examples of typical features are bearing informationfrom a radar system or edge orientations, texture, color or frequencyinformation from a visible image. The different feature classes andsubclasses are each assigned a different label, typically a number.

The labeled coordinates 24 include M labeled "real" coordinates 25 foreach target, as well as false or "ghost" coordinates 26. For pointobjects, the M real coordinates 25 are each an estimate of the target'scentroid 16. For area objects, the M coordinates are estimates ofrespective coordinates 16 on the target. The ghost coordinates are aresult of the inherent geometry problem, and the noisy featureextraction and labeling processes. In some cases, such as trackingairplanes from angle-only data, the tracking problem can be ill-posed,in which case the number of candidate coordinates 24 is necessarilygreater than the number of actual target coordinates 16.

The sensor sequentially outputs the candidate coordinates 24 withrespective labels 22 to a neural network 28 for deghosting and trackingthe targets 14. An estimate N 30 of the number of targets is alsoprovided as an input to the network. The estimate N is provided from aseparate source and can be based on typical values for a type of scene,visual information or intelligence reports. The neural network outputssets of labeled coordinates 32, which correspond to the positions ofpossible targets. As the network converges towards the real coordinates25, the neurons lock onto and track the real targets 14 and diverge awayfrom the ghost targets. With time, the accuracy and confidence level ofthe coordinates 32 increases.

The labeled coordinates 32 are provided as inputs to a post-processor34, which selects the higher confidence coordinates as real targets anddiscards the lower confidence ones as ghost targets. The post-processorcan also use other information to filter out the ghost targets. Forexample, if the topography of the scene is known, the post-processorcould disqualify coordinates that place vehicles in a lake or thatposition airplanes inside a mountain. Furthermore, higher levelprogramming techniques could be used, for example, to analyze the pathof the coordinates. A highly random path would be disqualified becauseit does not conform to the possible paths of the targets.

The post-processor assigns a symbol 36 to each of the selected sets oflabeled coordinates 32. The symbols' coordinates 38 are set equal to thecentroids of the respective coordinate sets. The symbols are displayedon a video display 40 and track the movement of the targets 14.Alternatively, or in addition to displaying the symbols, thepost-processor may store the coordinates and tracking information in amemory 41 for subsequent analysis.

FIG. 3 is a block diagram that illustrates the generation of the labeledinput coordinates 24 for point objects using angle-only data. This typeof system is used to deghost and track aircraft. Three sensors 42a-42ce.g., radar units, continuously detect and output the approach angles orbearings for the targets. Three processing units 44a-44c receivemutually exclusive pairs of the angle data as inputs. Knowing thecoordinates for each of the sensors, the processing units compute thecoordinates 24 at which the pairs of angles intersect and assign themrespective labels 22, i.e., 1, 2 or 3. A coordinate labeled with a 1 wasproduced by the first processing unit 44a and represents theintersection of the angles sensed by sensors 42a and 42b, and similarlyfor labels 2 and 3. A multiplexer 46 polls each of the processing units,1,2,3,1,2,3, . . ., and periodically outputs the labeled coordinates 24to the neural network 28.

FIG. 4 is a diagram of a possible targeting scene 12 for thethree-sensor multiple target system shown in FIG. 3. Ideally, at any onetime, the three sensors 42a-42c would detect the approach angles of thesame target 14a, 14b or 14c, and hence the three labeled coordinates 24found by intersecting the angles would each be an estimate 25 of thattarget's position. Because the angles are some-what noisy, the estimates25 form small triangles for each real target 14. The triangles'centroids provide reasonably accurate estimates of the targets',positions.

However, in typical systems, the sensors randomly detect the approachangles of the targets such that the labeled coordinates 24 aresubstantially random and include both the targets' estimated coordinates25 and the ghost coordinates 26. The ghost coordinates are a result ofintersecting angles that correspond to different targets. Ghost targets50 are defined by sets of M ghost coordinates 26, each coordinate havinga different one of the feature levels 22. The ghost targets arerepresented by triangles which tend to be larger than those associatedwith the real targets, and will break up and reform at different placesin the scene as the real targets move around.

The number of potential labeled coordinates is on the order of MN², withonly MN of them being the estimated target coordinates 25. Therefore, asthe number of targets increases, the complexity of suppressing the ghostcoordinates i.e., deghosting, and tracking the targets increasesexponentially. The neural network architecture 28 and tracking methodembodied in the present invention provide a low complexity andrelatively accurate approach for tracking targets.

FIG. 5 is a diagram of the MEFN 28 used in the present invention todeghost and track the targets 14. The MEFN uses N independent sets 54 ofneurons. Each set includes M feature specific neurons 56 withcoordinates 57. The neurons each have different labels 58A-58M which arein one-to-one correspondence with the labeled estimated coordinates 25for each target. For example, for the three-sensor angle-only trackingsystem shown in FIGS. 2 and 3, M=3 and each set 54 includes neuronslabeled 1, 2 and 3.

Each neuron 56 has an associated adaptive receptive field r_(ij) ^(t) 59that is a function of a distortion parameter p_(ij) ^(t) 60, a lockingparameter h_(ij) ^(t) 62 and an expectation parameter e_(ij) ^(t) 64.The i^(th) subscript identifies the neuron set the j^(th) subscriptidentifies the neuron in the set and the t superscript is the timeindex.

The receptive field defines a radius around the neuron in which it mayrespond to an input. When the network is initiated all of the receptivefields are large. As the neurons converge, the receptive field isreduced so that the neurons are only sensitive to the local movements ofa particular target coordinate. If the neuron converges to a ghostcoordinate, its receptive field will increase until it breaks away.

The locking parameter 62 is proportional to the previous changes in theneuron's coordinates. As the neuron converges towards a target'scoordinate, its locking parameter is reduced, which in turn reduces itreceptive field. Conversely, if the neuron's coordinates changesubstantially, its locking parameter will increase. The lockingparameter provides memory based upon the neuron's prior movement, andhence tends to change much slower than the neuron's receptive field.

The expectation parameter 64 is proportional to the elapsed time (numberof iterations) since the neuron was last changed by a candidatecoordinate 24. On average each neuron should be selected one out ofevery MN² iterations. If the neuron is not selected, it may haveconverged toward a ghost coordinate that no longer exists, or itsreceptive field may be too small. In either case, the parameter and thereceptive field are increased until the neuron is selected, at whichpoint its expectation parameter is reset to its initial value.

The distortion parameter 60 is a function of the neurons' coordinates57. For point objects, the distortion decreases as the coordinatesconverge towards one of the target's centroids. The perimeter of thegeometric shape formed by the set of neurons is a global distortionparameter, i.e., one that is the same for each neuron in the set. Thesum of the distances between a neuron and each of the other neurons inthe set is a local parameter, i.e., one that is specific to each neuron.For area objects, the distortion decreases as the size and shape of theset of neurons conforms to the scaled template.

The estimated coordinates 25 associated with the real targets tend toform smaller shapes (triangles) than do the ghost coordinates 26 and aremore consistent over time. At a given instant, a set of ghostcoordinates may form a relatively small shape, causing the neurons toconverge towards the associated ghost target. However, the movement ofthe targets in the scene causes the ghost object to grow quickly andbreak up. Therefore, the sets of neurons will tend to converge towardsthe smaller and more consistent shapes generated by the estimatedcoordinates 25 and diverge away from the ghost targets. The neurons'receptive fields are reduced as the associated distortion parameterdecreases.

FIG. 6 is a flowchart illustrating the MEFN 28 for deghosting andtracking the targets 14. In addition to the receptive field r_(ij) ^(t),and locking h_(ij) ^(t), expectation e_(ij) ^(t) and distortion p_(ij)^(t) parameters, the MEFN uses a number of other parameters which aredefined as follows:

x=Vector of the input coordinates 24.

m_(ij) ^(t) =Vector of the neurons' coordinates 57.

F_(ij) =Feature type associated with neuron ij 58.

F(x)=Feature type of input point x.

k_(ij) ^(t) =Counter for e_(ij) ^(t).

l_(ij) ^(t) =Counter for h_(ij) ^(t).

h^(/t) _(ij) =Low gain locking parameter, h^(/t) _(ij) <h_(ij) ^(t).

α=Learning rate of selected neuron.

α'=Learning rate of other neurons in selected set.

elast=The elasticity parameter of the network.

S(z)=A bounded and monotonically increasing function for e_(ij) ^(t) andh_(ij) ^(t), e.g., the Sigmoid activation function ##EQU1## λ_(h),λ_(e)=Maximum values for h_(ij) ^(t), and e_(ij) ^(t), respectively.

g_(h),g_(h'),g_(e) =Sigmoid gain parameters for h_(ij) ^(t), h^(/t)_(ij) and e_(ij) ^(t), respectively, with g_(h') <g_(h).

τ,λ₁,λ₂ =Annealing decay, start and end values for the receptive field.

τ_(h) =Decay term for selected h_(ij) ^(t).

δ_(e) =Increment size for all e_(ij) ^(t).

ε=Minimum receptive field size.

The ranges and typical values for the fixed parameters for thethree-sensor angle-only tracking system are as follows:

0<α<1 with a typical value of 0.9,

elast= 1,50! with a typical value of 35,

k₀ =-40, l₀ =+40,

λ_(h) = 1.0,50!, with a typical value of 2,

λ_(e) = 10,100!, with a typical value of 50,

g_(h) = 0.05,0.5!, with a typical value of 0.2,

g_(h') <g_(h) = 0.01,0.45!, with a typical value of 0.02,

g_(e) = 0.05,0.5!, with a typical value of 0.2,

τ_(h) = 1,30!, with a typical value of 12,

δ_(e) = 0.01,2!, with a typical value of 0.02,

λ₁ = 1,10!, with a typical value of 2,

¹ λ₂ = 0.2,5!, with a typical value of 0.75, and

τ is selected such that the receptive fields annealing term is halfwaybetween λ₁ and λ₂ at 200<t<1000 iterations.

As shown in FIG. 6, the coordinates m_(ij) ^(t) for each set of neurons56 are randomly initialized with values inside a specified region ofinterest (step 66), and the time parameter is set equal to zero (step68). In step 70, the parameters are initialized as follows:

The distortion parameter for point objects can be either its perimeter,##EQU2## or a local distortion of each neuron ##EQU3## The distortionparameter for area objects is given by: ##EQU4## where δ_(ijl) definesthe template of the object's shape, γ_(ij) is the scaling parameter, iselects the object, j and l select points on the object and M_(i) is thenumber of points on the selected object. ##EQU5##

The deghosting and tracking process is initiated in step 72 by selectingthe first candidate coordinate ×25 with label F(x). In the next step 74,each of the neurons 56 having the same label 58 as the candidatecoordinate and having receptive fields that are large enough to includex, are included in a set A. Formally,

    A={m.sub.ij.sup.t-1 |∥m.sub.ij.sup.t-1 -x∥≦r.sub.ij.sup.t-1 F.sub.ij =F(x)}.     (9)

If set A is empty (step 76), control returns to step 72 and the nextcandidate coordinate is selected. Otherwise, the algorithm selects theneuron in set A that is closest to x (step 78). The selected neuron isdesignated by (st) with coordinates m_(st) ^(t). In step 80, theselected neuron's coordinates and the other coordinates in its set aremoved towards x: ##EQU6##

The scale factor c is equal to α for the selected neuron (st) and isequal to α' for the other neurons in its set.

The expectation parameters are updated in step 82 as follows:

    k.sub.ij.sup.t =k.sub.ij.sup.t-1 +δ.sub.e ∀(i≠sj≠t), and k.sub.st.sup.t =k.sub.0, so that

    e.sub.ij.sup.t =λ.sub.e S(g.sub.e k.sub.ij.sup.t) ∀i,∀j.                              (12)

The expectation parameter is bounded but increases at every iterationunless its neuron is selected. Once a neuron is locked onto a targetcoordinate, if the signal (coordinate) disappears the expectationparameter will increase, causing the receptive field to increase, andthe neuron will move towards another target.

The distortion parameters p_(sj) ^(t) ∀j are recomputed in accordancewith the selected equation, 1, 2 or 3 (step 84). The distortionparameters are reduced as the sets of neurons converge towards thetargets, and are increased if the neurons mistakenly move towards one ofthe ghost targets.

The locking parameters are updated in step 86 as follows: ##EQU7##

The locking parameter increases when the change in the neuron'scoordinates exceeds the threshold and is reduced otherwise. By makingthe locking parameter counter a function of h', the counter is boundedand the locking parameter has a "hysterisis" effect or memory, whichimproves the algorithms convergence and tracking.

In step 88, the neurons' receptive fields are updated according to:

    r.sub.ij.sup.t = λ.sub.1 exp(-t/τ)+λ.sub.2 !p.sub.ij.sup.t h.sub.ij.sup.t +e.sub.ij.sup.t +ε∀i,∀j.                    (16)

The receptive field is proportional to the product of the distortion andlocking parameters weighted by a decaying exponential of time, plus theexpectation parameter and minimum radius ε. The minimum radius ensuresthat the neurons can track moving targets. Reducing the receptive fieldsas a function of time reinforces the neural net's convergence andtracking properties.

As a set of neurons converges towards one of the target's, thedistortion, locking and expectation parameters are reduced and maintainrelatively low values. Hence, the neurons' receptive fields becomerelatively small and only respond to and track movements of the target.The receptive fields will increase, causing the neurons to move towardsother targets if the distortion, expectation or locking parametersincrease.

In step 90, ##EQU8##

As the neurons converge, the distortion parameter decreases so thatα'_(ij) is reduced, and the set of neurons conforms to the shape of thetarget's coordinates. The neurons' coordinates 32 and associatedparameters are output to the post-processor 34.

In step 92, the time is incremented and the next labeled candidatecoordinate x 24 is selected in step 72. The coordinates are read in fromthe multiplexer until the neural net is reset.

If the MEFN is applied to a static scene, the targets' coordinates areread in repeatedly until the network converges to the targets. Theadaptive receptive field and the locking, distortion and expectationparameters ensure that the network will converge to the smallestdistortion targets, e.g., triangles. Because the scene is static, thedynamic properties of the MEFN that eliminate the ghost targets are notapplicable, and thus a few relatively small ghost targets may beidentified as real targets.

FIG. 7 is a flowchart of the post-processor 34. In step 94 the rawneuron data, i.e., coordinates 32 and the associated parameters, isfiltered to identify the sets of neurons with the highest confidencelevels (most likely to indicate real targets). Some of the sets ofneurons might not be fully converged or may be temporarily stuck in alocal minima (ghost targets). Furthermore the estimate N 30 mayaccidentally or intentionally overestimate the actual number of targets14. Intentionally overestimating the number provides more neuron sets,thus increasing the probability that all of the targets will bedetected. However, the additional neurons increase the complexity of theMEFN.

The sets of neurons can be filtered by (step 96) selecting the L<N setsthat have the smallest locking parameters. The receptive fields ordistortion parameters could also be used, but the locking parameter is amore accurate and stable measure. Alternatively, higher level orcognitive processing (step 98) can be performed on the data to removesets that are not realistic for the tracking problem. For example, theprocessor might monitor the paths followed by the sets' centroids andsuppress those that do not make sense, e.g. a plane flying in tightcircles or through a mountain.

In step 100, the centroids 36 for the remaining sets are computed and arepresentative symbol 38 is assigned to each centroid. The pairs ofsymbols and centroids are provided as inputs to the memory 41 and/or thedisplay 40. The displayed symbols are updated approximately every 20iterations to detect and track the movement of the targets 14 in thescene 12.

FIG. 8 is a diagram illustrating one iteration of the MEFN 28, in whichthree sets 54a-54c, each containing three neurons 56 that are labeled 1,2 and 3 respectively, and a candidate coordinate x 24 which is labeledwith feature type 1, are shown. The candidate coordinate is locatedwithin the receptive fields for the type 1 neurons from sets 54a and54b, and is closer to the neuron in 54a. The coordinates of the neuronsin set 54a move towards the candidate coordinates and their associatedparameters are updated as described previously.

FIGS. 9a through 9c are diagrams that illustrate the MEFN's initialstate, a partially converged state and the converged state. The ghostcoordinates are not shown. Initially, the neurons are spaced far apartand have very large receptive fields so that they can respond to thetargets' actual coordinates 25. As the MEFN converges, the neurons movetowards the respective labeled coordinates and their receptive fieldsare reduced. When the neurons lock onto the respective coordinates, thereceptive field collapses to approximately its minimum value so that theneurons will only respond to and track movements of that particularcoordinate.

The multiple elastic feature network incorporates multiple independentsets of feature specific neurons to deghost and track targets. Eachneuron includes an adaptive receptive field that is a function ofdistortion, expectation and locking parameters, which utilize thetemporal characteristics of the targeting scene to lock onto and trackthe targets. The MEFN provides a robust and low complexity approach tothe deghosting and tracking problem. It is capable of handling in excessof forty targets simultaneously, locks onto the targets withinapproximately 1000 iterations and has a relatively low error rate.

While several illustrative embodiments of the invention have been shownand described, numerous variations and alternate embodiment will occurto those skilled in the art. Such variations and alternate embodimentsare contemplated, and can be made without departing from the spirit andscope of the invention as defined in the appended claims.

I claim:
 1. A target deghosting and tracking system, wherein a pluralityof targets are each represented by a set of M time varying coordinateswhich are each labeled with a feature type, comprising:a sensor forsensing labeled candidate coordinates which are either one of thetargets' M coordinates or labeled ghost coordinates; a multiple elasticfeature net having N sets of M feature specific neurons, each neuron andits coordinates being labeled with a different one of said feature typesand having an adaptive receptive field and a distortion parameter, saidneurons moving towards similarly labeled candidate coordinates that liewithin their receptive fields to reduce said distortion parameters, thuscausing said sets of neurons to converge towards and track respectivetargets and diverge away from said ghost coordinates; and a display fordisplaying respective target symbols that track the sets of neurons, andhence the targets.
 2. The system of claim 1, wherein each neuron furthercomprises:an expectation parameter that measures the time elapsed sincethe neuron was last moved, increasing said expectation parameter causessaid neurons to diverge away from said ghost coordinates; and a lockingparameter that is proportional to previous movements in the neuron'scoordinates, decreasing said locking parameter causes said neuron toconverge to said object coordinates.
 3. A target deghosting and trackingsystem, wherein a plurality of targets are each represented by a set ofM time varying coordinates which are each labeled with a feature type,comprising:a sensor for sensing labeled candidate coordinates which areeither one of the targets' M coordinates or labeled ghost coordinates; amultiple elastic feature net having N sets of M feature specificneurons, each neuron and its coordinates being labeled with a differentone of said feature types and having a distortion parameter and anadaptive receptive field, each neuron's adaptive receptive field beingupdated as a function of its distortion parameter and increasing as theelapsed time since the neuron was last selected increases, said neuronsmoving towards similarly labeled candidate coordinates that lie withintheir receptive fields to reduce said distortion parameters, thuscausing said sets of neurons to converge towards and track respectivetargets and diverge away from said ghost coordinates; and a display fordisplaying respective target symbols that track the sets of neurons, andhence the targets.
 4. A target deghosting and tracking system, wherein aplurality of targets are each represented by a set of M time varyingcoordinates that specify the target's predetermined size and shape, eachcoordinate in the set being labeled with a feature type, comprising:asensor for sensing labeled candidate coordinates which are either one ofthe targets' M coordinates or labeled ghost coordinates; a multipleelastic feature net having N sets of M feature specific neurons, eachneuron and its coordinates being labeled with a different one of saidfeature types and having a distortion parameter that is the deformationof the set of neurons with respect to a scaled template of the target'spredetermined size and shape and an adaptive receptive field that isupdated as a function of its distortion parameter, said neurons movingtowards similarly labeled candidate coordinates that lie within theirreceptive fields to reduce said distortion parameters and the totaldeformation of said N sets of neurons, thus causing said sets of neuronsto converge towards and track respective targets and diverge away fromsaid ghost coordinates; and a display for displaying respective targetsymbols that track the sets of neurons, and hence the targets.